TurboTest: Learning When Less is Enough through Early Termination of Internet Speed Tests

Internet speed tests are indispensable for users, ISPs, and policymakers, but their static flooding-based design imposes growing costs: a single high-speed test can transfer hundreds of MB, and collectively, platforms like Ookla, M-Lab, and Fast.com generate petabytes of traffic each month. Reducing this burden requires deciding when a test can be stopped early without sacrificing accuracy. We frame this as an optimal stopping problem and show that existing heuristics—static thresholds, BBR pipe-full signals, or throughput stability rules from Fast.com and FastBTS—capture only a narrow slice of the achievable accuracy—savings trade-off. This paper introduces TURBOTEST, a systematic framework for speed test termination that sits atop existing platforms. The key idea is to decouple throughput prediction (Stage~1) from test termination (Stage~2): Stage~1 trains a regressor to estimate final throughput from partial measurements, while Stage~2 trains a classifier to decide when sufficient evidence has accumulated to stop. Leveraging richer transport-level features (RTT, retransmissions, congestion window) alongside throughput, TURBOTEST exposes a single tunable parameter ε for accuracy tolerance and includes a fallback mechanism for high-variability cases. Evaluation on 1 million M-Lab NDT speed tests (2024–2025) shows that TURBOTEST achieves 1.8-4.4× higher data savings than an approach based on BBR signals while reducing median error. These results demonstrate that adaptive ML-based termination can deliver accurate, efficient, and deployable speed tests at scale.